Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology
<p dir="ltr">We explore THz communication uplink multi-access with multi-hop Intelligent reflecting surfaces (IRSs) under correlated channels. Our aims are twofold: 1) enhancing the data rate of a desired user while dealing with interference from another user and 2) maximizing the co...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2024
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| _version_ | 1864513545102688256 |
|---|---|
| author | Muhammad Shehab (16904880) |
| author2 | Mohamed Elsayed (3524918) Abdullateef Almohamad (16870074) Ahmed Badawy (6992093) Tamer Khattab (16870086) Nizar Zorba (16888728) Mazen Hasna (16904661) Daniele Trinchero (16904886) |
| author2_role | author author author author author author author |
| author_facet | Muhammad Shehab (16904880) Mohamed Elsayed (3524918) Abdullateef Almohamad (16870074) Ahmed Badawy (6992093) Tamer Khattab (16870086) Nizar Zorba (16888728) Mazen Hasna (16904661) Daniele Trinchero (16904886) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Shehab (16904880) Mohamed Elsayed (3524918) Abdullateef Almohamad (16870074) Ahmed Badawy (6992093) Tamer Khattab (16870086) Nizar Zorba (16888728) Mazen Hasna (16904661) Daniele Trinchero (16904886) |
| dc.date.none.fl_str_mv | 2024-01-23T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcoms.2024.3357701 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Terahertz_Multiple_Access_A_Deep_Reinforcement_Learning_Controlled_Multihop_IRS_Topology/29445998 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Machine learning Artificial intelligence multi-access communication sub-millimeter wave communication communication system performance Optimization Reflection Wireless communication Uplink Propagation losses Correlation Array signal processing |
| dc.title.none.fl_str_mv | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">We explore THz communication uplink multi-access with multi-hop Intelligent reflecting surfaces (IRSs) under correlated channels. Our aims are twofold: 1) enhancing the data rate of a desired user while dealing with interference from another user and 2) maximizing the combined data rate. Both tasks involve non-convex optimization challenges. For the first aim, we devise a sub-optimal analytical approach that focuses on maximizing the desired user’s received power, leading to an over-determined system. We also attempt to use approximate solutions utilizing pseudo-inverse (P<sub><em>i</em></sub><sub><em>n</em></sub><sub><em>v</em></sub>) and block solution <i>(</i><i>B</i><i>L</i><i>S</i><i>)</i> based methods. For the second aim, we establish a loose upper bound and employ an exhaustive search <i>(</i><i>E</i><i>S</i><i>)</i> . We employ deep reinforcement learning (DRL) to address both aims, demonstrating its effectiveness in complex scenarios. DRL outperforms mathematical approaches for the first aim, with the performance improvement of DDPG over the block solution ranging from 8% to 57.12%, and over the pseudo-inverse ranging from 41% to 190% for a correlation-factor equal to 1. Moreover, DRL closely approximates the <i>E</i><i>S</i><i> </i>for the second aim. Furthermore, our findings show that as channel correlation increases, DRL’s performance improves, capitalizing on the correlation for enhanced statistical learning</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2024.3357701" target="_blank">https://dx.doi.org/10.1109/ojcoms.2024.3357701</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_07c4949276e7764ff540ac46d0861f09 |
| identifier_str_mv | 10.1109/ojcoms.2024.3357701 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29445998 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS TopologyMuhammad Shehab (16904880)Mohamed Elsayed (3524918)Abdullateef Almohamad (16870074)Ahmed Badawy (6992093)Tamer Khattab (16870086)Nizar Zorba (16888728)Mazen Hasna (16904661)Daniele Trinchero (16904886)EngineeringElectrical engineeringInformation and computing sciencesMachine learningArtificial intelligencemulti-access communicationsub-millimeter wave communicationcommunication system performanceOptimizationReflectionWireless communicationUplinkPropagation lossesCorrelationArray signal processing<p dir="ltr">We explore THz communication uplink multi-access with multi-hop Intelligent reflecting surfaces (IRSs) under correlated channels. Our aims are twofold: 1) enhancing the data rate of a desired user while dealing with interference from another user and 2) maximizing the combined data rate. Both tasks involve non-convex optimization challenges. For the first aim, we devise a sub-optimal analytical approach that focuses on maximizing the desired user’s received power, leading to an over-determined system. We also attempt to use approximate solutions utilizing pseudo-inverse (P<sub><em>i</em></sub><sub><em>n</em></sub><sub><em>v</em></sub>) and block solution <i>(</i><i>B</i><i>L</i><i>S</i><i>)</i> based methods. For the second aim, we establish a loose upper bound and employ an exhaustive search <i>(</i><i>E</i><i>S</i><i>)</i> . We employ deep reinforcement learning (DRL) to address both aims, demonstrating its effectiveness in complex scenarios. DRL outperforms mathematical approaches for the first aim, with the performance improvement of DDPG over the block solution ranging from 8% to 57.12%, and over the pseudo-inverse ranging from 41% to 190% for a correlation-factor equal to 1. Moreover, DRL closely approximates the <i>E</i><i>S</i><i> </i>for the second aim. Furthermore, our findings show that as channel correlation increases, DRL’s performance improves, capitalizing on the correlation for enhanced statistical learning</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2024.3357701" target="_blank">https://dx.doi.org/10.1109/ojcoms.2024.3357701</a></p>2024-01-23T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcoms.2024.3357701https://figshare.com/articles/journal_contribution/Terahertz_Multiple_Access_A_Deep_Reinforcement_Learning_Controlled_Multihop_IRS_Topology/29445998CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294459982024-01-23T09:00:00Z |
| spellingShingle | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology Muhammad Shehab (16904880) Engineering Electrical engineering Information and computing sciences Machine learning Artificial intelligence multi-access communication sub-millimeter wave communication communication system performance Optimization Reflection Wireless communication Uplink Propagation losses Correlation Array signal processing |
| status_str | publishedVersion |
| title | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology |
| title_full | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology |
| title_fullStr | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology |
| title_full_unstemmed | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology |
| title_short | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology |
| title_sort | Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology |
| topic | Engineering Electrical engineering Information and computing sciences Machine learning Artificial intelligence multi-access communication sub-millimeter wave communication communication system performance Optimization Reflection Wireless communication Uplink Propagation losses Correlation Array signal processing |